A socioeconomic analysis of commuting professionals

Abstract

Everyday commuting as a mobility phenomenon is well-investigated and has been the topic of many contributions.
Nevertheless, the distinct determinants of the commuting professional’s motivation to regularly travel comparably long distances have not been in the focus of research yet.
Thus, this contribution analyses the sociodemographic variables that underpin the well-educated group’s decision to commute longer distances than other educational groups. For German Microcensus data, ordered logistic regression models are used to estimate and compare the influences of sociodemographic variables on all commuting employees and commuting professionals.
The data of German Microcensus of the year 2012 are used for the analysis. The results imply that some characteristics exert the already known effects on both samples. Others do vary with education and thus illustrate some unique sociodemographic influences on the commuting behaviour of professionals.

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Transportation (2021) 48:2127–2158
https://doi.org/10.1007/s11116-020-10124-w
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A socioeconomic analysis ofcommuting professionals
MoritzKersting1,5· EikeMatthies2,3· JörgLahner3· JanSchlüter4,5
Published online: 17 August 2020
© The Author(s) 2020
Abstract
Everyday commuting as a mobility phenomenon is well-investigated and has been the
topic of many contributions. Nevertheless, the distinct determinants of the commuting
professional’s motivation to regularly travel comparably long distances have not been in
the focus of research yet. Thus, this contribution analyses the sociodemographic variables
that underpin the well-educated group’s decision to commute longer distances than other
educational groups. For German Microcensus data, ordered logistic regression models are
used to estimate and compare the influences of sociodemographic variables on all commut-
ing employees and commuting professionals. The data of German Microcensus of the year
2012 are used for the analysis. The results imply that some characteristics exert the already
known effects on both samples. Others do vary with education and thus illustrate some
unique sociodemographic influences on the commuting behaviour of professionals.
Keywords Commuting· Professionals· Education· Occupation· Microcensus· Germany
Introduction
Occupational mobility is undergoing continuous and long-run changes. The impacts of dig-
itisation, an increase of knowledge-intensive activities and changes in sociodemographic
patterns reduce the dependence of residential and workplace choice especially for pro-
fessional workers (Alexander and Dijst 2012; Strambach and Kohl 2015; Kakihara and
Sørensen 2004; He and Hu 2015). Hence, the average commuting distance throughout
* Jan Schlüter
jan.schlueter@ds.mpg.de
1 Chair ofStatistics, Department ofEconomics, Georg-August-University ofGöttingen,
Humboldtallee 3, 37073Göttingen, Germany
2 Department ofEconomics, Georg-August-University ofGöttingen, Humboldtallee 3,
37073Göttingen, Germany
3 Chair ofEconomic Development andCorporate Governance, Faculty ofResource Management,
Hochschule für angewandte Wissenschaft und Kunst, Büsgenweg 1a, 37077Göttingen, Germany
4 Institute fortheDynamics ofComplex Systems, Faculty ofPhysics, Georg-August-University
ofGöttingen, Friedrich-Hund-Platz 1, 37077Göttingen, Germany
5 NGM, Department ofDynamics ofComplex Fluids, Max-Planck-Institute forDynamics andSelf-
Organization, Am Fassberg 17, 37077Göttingen, Germany
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Europe increased over the last years (Einig and Pütz 2007; Barker and Connolly 2006;
Haas and Hamann 2008; Aguilera 2005; Eurostat 2019; Ruppenthal and Lück 1999).
From the labour demand’s perspective, this increased mobility provides new opportuni-
ties to face several challenges of the new century. The impact of contemporary and future
problems on the labour markets such as a shortage of skilled professionals, the rural exodus
or demographic changes can be mitigated by enabling suitable surplus labour to move fast
and easy from one region to another (Shearmur and Motte 2009; Granato etal. 2009; Epi-
fani and Gancia 2005; Suedekum 2005).
Then again, several problems arise from increased mobility. Phenomena such as air
pollution, climate change induced by greenhouse gas or congestion are common knowl-
edge and often associated with motorised individual transport (Nobis and Kuhnimhof
2018). Moreover, spatial mismatches in terms of job-housing imbalances or the commut-
ers’ access to certain means of transport are determined by factors such as housing-prices,
neighbourhoods, segregation or growing sprawls (Ewing etal. 2003; Horner and Mefford
2007; Zhu etal. 2019; Sultana 2002, 2005).
Thus, the task of policy and decision-makers is to provide green, fair and appropriate
infrastructural frameworks that vary by region and the corresponding user-groups’ charac-
teristics. Next to new technological opportunities in terms of smart, connected and green
mobility, a deep understanding of the determinants of an individuals’ mobility demand is
indispensable.
As previous research suggests, contemporary changes in individual commuting patterns
are determined by several social, economic and geographic characteristics such as age,
gender, place of residence, type of settlement, workplace choice, income or qualification
(Wagner and Mulder 2015; Auspurg and Schönholzer 2013; Sultana 2002; Weber and Sul-
tana 2008; Granato etal. 2009; Ewing etal. 2003). Moreover, the presence of other house-
hold members such as a partner or children and their characteristics seem to exert an inter-
dependent influence on an individual’s commuting behaviour (Auspurg and Schönholzer
2013; Sultana 2003, 2005; Surprenant-Legault etal. 2013; Wagner and Mulder 2015; Vuk
etal. 2016; Hjorthol and Vågane 2014; Green 1997).
By several interdisciplinary contributions, numerous phenomena were found to be
related or interdependent to the transitions in the job-related mobility of employees. From
a medical perspective, commuting has a clear negative effect on the health of an individual.
Stress and time pressure are common proxies to measure the impact of commuting on the
prevalence of diseases such as high blood pressure and psychological problems (Evans and
Wener 2006; Gottholmseder et al. 2009). The results of e.g. Roberts et al. (2011), Clark
etal. (2019) and Rüger and Schulze (2016) indicate that the health of women, in particu-
lar, is negatively affected by commuting. As the presence of children in the household
reinforces the effect, the difference is explained by the often more complex everyday life
of women, which tends to involve longer trip chains, e.g. due to the escorting of children
(Scheiner and Holz-Rau 2017; Motte-Baumvol etal. 2017; Oakil etal. 2016).
Many further implications on the connection of mobility and gender emerge from the
social sciences. Gender-specific role models, differences in occupational choice, income
and family structures have already been validated as both cause and symptom of the diver-
gent mobility patterns of women and men by numerous contributions (Auspurg and Schön-
holzer 2013; Manderscheid 2016; Hjorthol and Vågane 2014; Wagner and Mulder 2015;
Augustijn 2018; Rapino and Cooke 2011). Nevertheless, there is evidence, that these dif-
ferences dilute under certain circumstances (Sultana 2005).
Economic themes in literature mainly consider sociodemographic characteristics such as
qualification, family structure and gender as an explanation for an employee’s commuting
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behaviour (Pfaff 2012; Manderscheid 2016; McQuaid and Chen 2012). Moreover, resi-
dential and workplace choices of employees were found to be dependent on the attributes
of a region, such as unemployment, wage levels, housing prices or the economic struc-
ture (Swärdh and Algers 2016; Roberts etal. 2011; Shearmur and Motte 2009; Reichelt
and Haas 2015; Zhu etal. 2019). Specific entanglements appear to exist between urban
and rural areas (Sultana 2003; Champion 2009; Renkow and Hoover 2000; Bosworth and
Venhorst 2018) as well as regions with differences in economic prosperity like West and
East Germany (Bosworth and Venhorst 2018; Einig and Pütz 2007; Granato etal. 2009;
Reichelt and Haas 2015; Haas 2012). Urban structure and the spatial distribution of socio-
economic properties form individual commuting patterns on a micro-level and thus influ-
ence the extent and efficiency of local transportation capacities (Horner etal. 2015; Kan-
aroglou etal. 2015; Zhou and Murphy 2019; Layman and Horner 2010).
Whilst the effects of income on occupational mobility have been extensively discussed
in the literature, an employee’s qualification was usually used as a control. Contributions
such as Granato etal. (2009), Groot etal. (2012), Wrede (2013), Kakihara and Sørensen
(2004) and Alexander and Dijst (2012) suggest an impact of education on residential loca-
tion choice and thus commuting distances on both the household and individual level.
Moreover, these patterns appear to vary with different economic and sociodemographic
characteristics and along qualification groups (Granato et al. 2009; Groot et al. 2012;
Wrede 2013; Sandow 2014).
To complement previous research, this contribution aims to analyse the sociodemo-
graphic determinants on the commuting behaviour of professionals. The results for highly
skilled employees are compared with the entirety of workers to emphasise the dedicated
effects. The basis for the analysis is the German Microcensus of 2012, which covers a large
number of respondents and attributes and hence allows to examine specific strata in detail.
The analysis proceeds as follows. The subsequent section provides an overview of the
current state of research regarding the links between commuting and the economic, spatial
and sociodemographic characteristics of employees. This overview is followed by the third
section, in which assumptions regarding the commuting behaviour of high-skilled workers
are formulated based on the presented theories and approaches. Subsequently, the results of
several ordered logistic regression models are presented and discussed.
Determinants andeects ofcommuting: abrief review oftheliterature
A considerable amount of literature examined the complex and interdependent determi-
nants and effects of commuting and used a variety of definitions and assumptions. One
straight approach defines all employees as commuters, that recurrently travel between
home and work. By that, almost all employees can be counted as commuters (Wagner and
Mulder 2015). Additional restrictions such as certain travel time or the commuting distance
are often used to achieve a finer distinction, e.g. 2 h per day (Schneider etal. 2002), 1 h
one-way (Kley 2012) or 30km one-way (Sandow 2014). Reichelt and Haas (2015) defined
those employees as commuters, whose residence is located in another county than the
place of work. Beyond prominent characteristics such as income or education, behavioural
approaches use measures like an individual’s stated willingness to commute or the valua-
tion of the time of other household members to explain the more complex dynamics within
household (Swärdh and Algers 2016). This section provides an overview of the common
approaches and findings of the determinants of commuting.
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Income, education andcommuting
In several European regions, the importance of job-related mobility is increasing in abso-
lute numbers (Eurostat 2019). For Germany, the amount of job-related passenger-kilome-
tres increased by roughly 9% and represents a share of more than 20% of the total mobility
(Nobis etal. 2019). The findings of the German Mobility Panel suggest that the commuting
distances are especially high for households with high education and high-income (Nobis
and Kuhnimhof 2018).
In a simplified economic model, travelling (and thus commuting) causes monetary and
non-monetary costs. While expenditures e.g. for fuel, tickets or vehicles account for the
first group, the second group contains other consequences such as negative health effects
or time costs. From an economic perspective, an employee’s income influences commuting
behaviour in several ways. First, the income determines the amount of money, that can be
spent on commuting. Second, it provides compensation for negative non-monetary effects.
The impact of monetary costs on commuting behaviour is comparably simple to assess.
For instance, DeLoach and Tiemann (2012) explains variances in the number and behav-
iour of job-related car-drivers from the American Time Use Survey by internalising cor-
responding gasoline prices. High gasoline prices led to a decline in the number of drivers
and induced fuel-efficient driving. It is reasonable to assume that employees seek to reduce
not only monetary but also non-monetary costs e.g. by their mode choice or by pooling
their rides with colleagues (Gardner and Abraham 2007; Ferguson 1997). In the case of
carpooling, Buliung etal. (2010) found that saving money was no extraordinary incentive
for US and Canadian employees to share their rides to travel to work. Other factors, such
as a desire for socialising, reduced stress or environmental consciousness are suggested to
play an important role (Buliung etal. 2010; Ettema etal. 2012). In several countries, these
commuting behaviours are the target of policy measures. Taxes, subsidies and promotion
of certain transport systems are used to stimulate a certain behaviour, primarily to reduce
traffic jams and negative environmental effects (Vanoutrive etal. 2012; Potter etal. 2006).
Occupations carried out by better qualified and specialised workers are usually bet-
ter paid. Also, wages for knowledge and skill intensive work vary regionally much more
than wages for other jobs. This induces a higher ability to bear the monetary costs of com-
muting, inducing more flexible behaviour in the choice of residence and job-location of
skilled workers (Haas 2012; Groot etal. 2012; Sultana 2002). Furthermore, with increas-
ing specialisation, it becomes more unlikely to find a job in the immediate vicinity that
matches the individual qualification profile. There are several indications that the mobility
behaviour of highly qualified employees differs fundamentally from that of others, e.g. in
terms of the commuting distances (e.g. Dauth and Haller 2018; Groot etal. 2012; Haas
and Hamann 2008) or avoidance strategies such as weekend commuting (Rüger and Sulak
2017).
Whilst these basic economic mechanisms of commuting are widely understood, more
complex questions arise when they relate to other disciplines and approaches. At a glance,
the current state of research suggests occupational commuting to be embedded in an inter-
dependent and multidisciplinary framework.
Commuting andlife satisfaction
A row of contributions deals with the relationship between life satisfaction and work-related
mobility (see De Vos etal. 2013 for a comprehensive literature review). Life satisfaction is
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usually determined by surveys and represented by indicators or proxies. These analyses on
physiological and psychological well-being investigate the effects of occupational mobility
on the health of employees. Dissatisfaction, psychological pressure and other stresses were
identified as the common proxies by which mobility harms health and thus decreases a
persons life satisfaction (Rüger and Schulze 2016; Clark etal. 2019; Stutzer and Frey 2008;
Chatman et al. 2019). Moreover, occupational mobility causes temporal and monetary
costs. According to pure economic equilibrium theory, such negative effects of commut-
ing should be compensated by positive effects like a better job match or a higher income
(Pfaff 2014; Stutzer and Frey 2008). However, the literature mentioned above suggests that
these costs and benefits usually do not meet in the end and employees commute longer than
expected. To improve their travel satisfaction, employees would have to relocate their place
of work or residence (see Morris and Zhou 2018 for an exceptional case).
Based on the data of the German Socio-Economic Panel, Pfaff (2014); Stutzer and Frey
(2008) and Stutzer and Frey (2007) demonstrate, that the general life satisfaction decreases
with increasing commuting distances. Moreover, the findings of De Vos etal. (2019) sug-
gest higher travel satisfaction after a relocation that comes along with shorter travel dis-
tances. Nevertheless, in practice, numerous employees travel longer times and distances
than optimal and avoid relocation (Stutzer and Frey 2008; Ye etal. 2020). Thus, the non-
monetary costs caused by long commuting distances are not sufficiently compensated
by e.g. income-related effects. This economic mismatch, or rather commuting paradox
(Stutzer and Frey 2008) arises from several individual restrictions, such as family ties or
the high costs for relocating a workplace or residence (Ye etal. 2020; Stutzer and Frey
2007, 2008; Pfaff 2012; Chatman etal. 2019; Clark etal. 2019; Morris and Zhou 2018).
Nevertheless, especially professionals appear to benefit from the rise of information and
communication technologies as knowledge-intensive work becomes more flexible in spa-
tial, temporal or interaction patterns and can be adapted to household restrictions more eas-
ily, by means such as home office or working while commuting (Alexander and Dijst 2012;
Ettema etal. 2012; Kakihara and Sørensen 2004; He and Hu 2015; Clark etal. 2019).
The influence offamily andgender
On an individual level, longer commuting distances lead to a higher income as the number
of better-paid jobs increases with a workers’ search radius. These higher incomes can be
interpreted as compensation for higher travel expenses (Pfaff 2012; Stutzer and Frey 2007;
Swärdh and Algers 2016; Scheiner 2016). In the economic literature, it is also common
practice to explain commuting behaviour with spatial analyses. The individual choice of
residence and place of work depends on several trade-offs between factors such as rent-
levels, income, commuting stress and school- or residential quality. A combination of resi-
dence- and workplace location with a resulting commuting distance is chosen in theory, if
it maximises the benefit under all individual constraints (Borck and Wrede 2006; Einig and
Pütz 2007; Pfaff 2012; Reichelt and Haas 2015; Wrede 2013; Chatman etal. 2019; Sultana
2002).
Regarding the family context, previous research has focused on the joint location (and
thus commuting) decisions of couple households or families with children (Hjorthol and
Vågane 2014; Stutzer and Frey 2007; Green 1997; Manderscheid 2016; Pfaff 2012; Sultana
2005; Surprenant-Legault etal. 2013). It was observed with a few exceptions (see below)
that commuting distances increase with the size of the household. An educated presump-
tion is based on increasing migration costs with each additional household member, so
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that long-distance commuting often is the best strategy for individual household members
(Pfaff 2012). Another viable explanation is that each additional household member adds
more restrictions on the households common residence and workplace choices.
One can expect that couples (and thus families) as a joint household will optimise their
overall benefit when making location decisions (Stutzer and Frey 2007; Sultana 2005) and
that the person with the higher income or the higher local commitment will dominate the
choice of location (Green 1997). Auspurg and Schönholzer (2013) illustrate that the lower
commuting distances of women contribute to a lower income. An increase in the com-
muting distance of women on average also resulted in an increase in income, which can
be explained by a larger selection of jobs due to an increased search radius. No significant
increase was reported for men.
Among others, Scheiner (2016), McQuaid and Chen (2012), Hjorthol and Vågane
(2014) and Manderscheid (2016) use and summarise interdisciplinary approaches to
explain gender divergence, including theories of economics, sociology and gender stud-
ies. A fundamental change in the classic mobility strategies of men, women and joint
households can be observed due to a steadily rising female employment rate and chang-
ing employment patterns in developed countries (Pfaff 2012; Kümmerling 2015). Sum-
marising previous contributions, women work more often part-time than men, earn less
and therefore show on average less willingness or ability to commute. It has also been
observed that women in (hetero) couple relationships primarily take on family tasks such
as housework and childcare. As a result, their willingness to commute decreases and the
selection of well-paid jobs that match their qualification and specialisation decreases as
well as (Augustijn 2018; McQuaid and Chen 2012; Kümmerling 2015; Swärdh and Algers
2016; Scheiner 2016; Gutierrez 2018). Consistently, Scheiner and Holz-Rau (2017) found
by analysing German activity patterns that women’s travel behaviour (despite shorter travel
distances) is comparably more complex and efficient. This finding gives support to the gen-
eral theory of traditional role models with “male breadwinners” who work full time and
commute far, and “female housewives” conducting a variety of activities during the day.
Swärdh and Algers (2016) analysed survey data from the Stockholm region based on
a stated choice approach. Their findings give support to the theory of role models as an
explanation for the observed divergence in commuting distances. The main results are that
both spouses value the woman’s commuting time higher than the mans’. Thus, both would
prefer the man to commute longer distances than the woman.
Pfaff (2012) showed by using German Socioeconomic Panel data that, for Germany,
the number and the age of children influence the distance travelled by the parents. Again,
divergence by gender can be observed: While children have a negative effect on women’s
commuting distances, they seem to influence men’s behaviour in the opposite direction.
The effects are less striking for older children. In summary, children increase the probabil-
ity for men to commute and decrease the probability for women, especially those working
part-time (McQuaid and Chen 2012).
Nevertheless, Sultana (2005) and Surprenant-Legault etal. (2013) found for the North-
American metropolitan areas of Atlanta and Montreal, respectively, that the average com-
muting times of dual-earner household are below the commuting times of single-earner
households. Surprenant-Legault etal. (2013) found the influence of one partners’ commut-
ing distance on the others’ to be significantly positive. Thus, partners in dual-earner house-
holds were found to rather complement than to substitute each others commuting distances,
which is inconsistent with the findings mentioned above. When testing for gender differ-
ences, both wives and husbands of dual-earner households were found to travel shorter
distances than their counterparts from single-earner households (Sultana 2005). Moreover,
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one conclusion of the analyses is, that higher housing prices in proximity to the workplaces
exert a more striking impact on the commuting behaviour than sociodemographics.
In general, the overall negative but non-homogeneous impact of children on their par-
ents commuting distances is usually explained with the family members’ wish to spend
time together. During the ACTUM-project, scientists analysed activity-based travel chains
and their relation to the individual and mobility demand in Copenhagen (Wind 2012).
Using this data, Vuk etal. (2016) determined the influence of socioeconomic variables and
the household structure on general travel demand. One of their major findings is that the
presence of children increases the priority of family time and that this dependency seems
to be stronger with a younger age of the children. Their results support the more general
findings of a row of contributions such as Hjorthol and Vågane (2014), Swärdh and Algers
(2016), Pfaff (2012) and McQuaid and Chen (2012).
In short, gender-related research on occupational mobility suggests both the existence of
reinforcing path dependencies on an economic level and a strong impact of traditional role
models on women’s willingness and ability to commute.
Commuting fromaneconomic geographical perspective
As described above, income and occupation can be assumed to exert an influence on an
individual’s residential choice and thus the commuting behaviour. Naturally, economic
prosperity is not evenly distributed within a country. In the case of Germany, economic
differences between the eastern and the western parts of the country are widely known and
certain mobility dynamics can be assumed. Thus, several contributions have examined the
commuting and migration between East and West Germany. Einig and Pütz (2007) illus-
trate that the commuting distances in the areas with weaker labour markets in eastern Ger-
many are significantly higher than those in economically strong regions. A similar trend
exists for the commuting duration, that seems to intensify in the long-term trend (Winkel-
mann 2010). Preferred destinations for long-distance commuters are primarily regions that
are strong in economic terms. Since the German Reunification, a distinct mobility dynamic
has established in absolute numbers from the east to the west, in terms of both migra-
tion and commuting (Granato etal. 2009; Haas and Hamann 2008; Brautzsch 2017; Haas
2012).
Contributions such as Granato et al. (2009), Epifani and Gancia (2005), Suedekum
(2005) or Haas (2012) focus on regional economic perspective. Traditional economic
explanations suggest that both commuting and relocation is useful for balancing regional
labour-economic differences and smoothes regional differences in employment and wages.
In this framework, relocation and commuting decisions of workers are motivated by
low qualification-job fits or wages within a region. Thus, their mobility establishes new
steady states on the labour markets by balancing the spatial mismatch. On the contrary,
approaches of the New Economic Geography suggest that commuting and migration can
also exacerbate existing disparities.
However, characteristic occupational mobility occurs between East and West Germany.
The role of labour and housing market differences as the most important driving forces
of commuting is generally acknowledged (Bogai etal. 2014; Shearmur and Motte 2009;
Granato etal. 2009; Sultana 2005). Due to the emigration of workers from regions that are
often weak in economic terms, a brain drain in the already weak regions is an obvious sug-
gestion. Hence, several studies deal with the qualification level of commuters and migrants
(Granato etal. 2009; Haas 2012). In a longitudinal analysis of German Socio-Economic
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Panel data, Granato etal. (2009) show that among East German commuters and emigrants,
the smallest groups consist of the highly qualified workers and the largest of low-skilled
workers. Occupational mobility into the West German labour market is primarily achieved
by less qualified workers, while qualified workers are the dominant group in the opposite
direction. In absolute terms, eastern Germany has a negative balance in each group, but the
qualified segments are disproportionately less affected.
Since this fact contradicts the fundamental theories and empirical findings of mobile
professionals outlined above, the case of Eastern Germany has not been understood defi-
nitely. It is not yet known whether Eastern Germany is affected by a sustained migration of
talents to the West since the German Reunification. Although the migration and commut-
ing balances of highly qualified workers are often negative, other levels of education are
still much more affected. Hints for interdependence with sociodemographic characteristics
e.g. for gender, family variables and age are stated in Haas (2012), Granato etal. (2009)
and Haas and Hamann (2008).
Next to such macrogeographic implications on the level of states or regions, numerous
contributions analysed spatial mismatches on a micro-scale. Several studies found urban
form and network properties to contribute to mobility decisions and to be correlated with
sociodemographic variables such as income (Schleith et al. 2016; Schleith and Horner
2014), family variables (Sultana 2005), life cycle stages (Horner et al. 2015; Kim et al.
2005; Wood etal. 2016) or (especially in North American contexts) minority background
(Horner and Mefford 2007; Sultana 2005; Yang 2008). On a micro-scale, both individ-
ual and aggregated commuting patterns of workers are used to assess urban planning and
transportation systems’ efficiency. Excess commuting has established as one major concept
in order to evaluate to which extent phenomena such as urban sprawl or segregation extend
the daily commutes in a region (Kanaroglou etal. 2015; Horner etal. 2015; Korsu and Le
Néchet 2017; Suzuki and Lee 2012; Zhou and Murphy 2019; Layman and Horner 2010).
As the preceding summary demonstrates, an employee’s determinants of occupational
mobility do not stand on their own but are embedded in an interdependent network of char-
acteristics and relationships. Other attributes, such as gender or age, can also provide fur-
ther explanatory content in all the categories mentioned. Thus, the following section sets
up assumptions to conclude the theories and findings as described above.
Theories andassumptions ontheeveryday commuting
ofprofessionals
This contribution aims to evaluate the influence of economic, demographic and social vari-
ables on the commuting behaviour of professionals. As the state of research implies, these
characteristics are embedded in an interdependent relational network. Following the find-
ings of the previous research as described above, three subsets of assumptions are set up.
Occupation
The first part of this contribution analyses the impact of occupational variables on commut-
ing behaviour. As shown above, there is a broad consensus in the literature, that income and
commuting distance are positively correlated. On the one hand, higher income increases an
employee’s willingness to commute, as it compensates for higher costs and stress levels.
On the other hand, employees with a higher commuting radius cover a larger area and by
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that a comparable higher number of attractive job vacations. While the positive correlation
of income and distance turned out to be valid in general and especially for the middle-class
or lower educated worker, the effect has to be evaluated separately for professionals, as
their behaviour might underlie different constraints (Groot etal. 2012; Granato etal. 2009;
Wrede 2013). Since a high qualification level usually comes along with a higher degree of
specialisation, the distance to an appropriate job is likely to be higher compared to lower-
skilled employees. On the other hand, low unemployment rates for professionals suggest
a higher demand for skilled workers that could result in higher wages and lower distances
necessary to achieve an appropriate job match. Further on, professionals are less likely to
become unemployed than other groups of workers. In comparison, especially middle-class
workers are suggested to accept very long commuting distances to maintain their income
levels due to harder labour market conditions (Groot etal. 2012).
Assuming stress levels to increase with commuting distances and decreasing marginal
utility of income, it can be expected that an individuals maximum commuting distance
exists. Since the wages for higher qualified employees are usually higher than on average,
a marginal increase of both income and commuting distance might result in lower overall
utility. Thus, the willingness to commute long distances could be lower for highly qualified
employees than on average. Thus, the first assumption is:
A1.1: The influence of income on the commuting distance of professionals is posi-
tive.
As shown above, commuting is a strategy to avoid expensive and inconvenient relocation.
Thus, an employee only relocates if the costs of relocation are smaller in comparison to the
costs of commuting. It can be assumed that employees with fixed-term contracts are more
likely to choose commuting instead of relocation since they have to make new decisions
when their contract ends. Besides, the negative effects of commuting such as stress might
be less considered when they have to be endured only for a while. In comparison, part-time
workers usually receive a lower wage than an equivalent full-time worker, regardless of
their contracts’ duration. Since their income cannot compensate for the negative effects of
long commuting distances, their commuting times are comparably shorter.
A1.2: Professionals with fixed-term contracts commute longer distances than those
with permanent contracts.
A1.3: Part-time contracts exert a negative influence on commuting distances for all
samples and genders.
Managers and supervisors are usually better paid and more autonomous and independent
in their work. Since their occupational characteristics are linked to other variables as men-
tioned above, this special group likely exerts a distinct commuting behaviour. According to
McQuaid and Chen (2012), the commuting times of managers in the UK can be expected
to be distinctly higher than those of regular workers. In contrast, Sultana (2005) found for
employees in the Atlanta metropolitan area, that higher occupational status is linked to
shorter commutes. Since their findings are based on different geographical areas, the com-
parability is somewhat limited. Due to the study design and the location of the research
area, the resulting hypothesis for German employees is:
A1.4: Managers and supervisors commute longer distances than regular profession-
als or employees.
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Differences fromtheEast totheWest
As the state of research implies, labour demand and wages differ from East Germany to
West Germany. Although the indicators converge for several years, the western parts of the
country have generally lower unemployment rates and higher wages (Haas and Hamann
2008; Haas 2012; Granato etal. 2009; Fuchs etal. 2015; Krause 2019). In accordance, it
can be assumed that the commuting balance from the west to the east is positive and the
other way round. Consistently, Granato etal. (2009) and Haas (2012) validate this assump-
tions for the years 2000–2005 and 1999–2010. However, Granato etal. (2009) observed
that the share of highly qualified commuters from East to West is considerably smaller than
for other qualification groups, although especially professionals are well known for their
longer commuting distances as shown above. A viable economic explanation is that wage
levels and labour demand for professionals vary by region.
By analysing commuting data of the German Labour Agency, Haas and Hamann (2008)
concluded that highly qualified employees usually commute comparably longer distances.
However, they noticed that this difference was not as distinct in East as in West Germany.
On the one hand, their results go along with the theory, that higher wages induce higher
commuting distances. On the other hand, their findings are contradictory to the assump-
tion, that a weaker labour demand leads to higher commuting distances as professionals
endeavour to find a job which is both better paid and second matching their specialised
qualification. The assumption to check for an imbalance between East and West Germany
is:
A2.1: East German professionals do not commute as far as West German profes-
sionals. The effects are less pronounced for the entirety of employees.
Commuting onhousehold levels
So far, the commuting behaviour of professionals was analysed on an individual level. As
the state of research implies, location choices as a basis for commuting behaviour are made
on the household level. Thus, additional household members such as children or partners
translate into additional constraints, as different occupational or educational requirements
have to be considered. The effects on men and women distinct due to divergent valuation of
the spouses commuting time and role models (Vuk etal. 2016; Hjorthol and Vågane 2014;
Swärdh and Algers 2016; McQuaid and Chen 2012).
From an exogenous perspective, a household which consists of a couple optimises its
overall utility by choosing the location of residence and the places of work. Since the indi-
vidual distance to work is not the only constraint to be considered, the resulting individual
commuting times of couples should be longer than those of singles. Among others, the
findings of e.g. Morris and Zhou (2018) and Pfaff (2012) gives support to this assumption
but do not distinguish between educational groups.
From an endogenous perspective, household decisions are the result of bargaining. Pre-
vious analyses validated that the partner with the higher economic power is more likely to
be the winner of bargaining decisions considering the location choice. In contrast, the other
partner often acts as the households secondary earner, which can imply part-time work
or a job change after a households’ relocation. Hence, the partner with a lower income
is usually more restricted in mobility decisions and has to adapt to the primary earner’s
preferences. Thus, the resulting commuting distances are usually shorter (Auspurg and
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Schönholzer 2013; Wagner and Mulder 2015; Hjorthol and Vågane 2014; Green 1997).
On average, women receive lower wages than men and are often associated with traditional
gender roles. Thus, they can be assumed to often take the role of the households’ second-
ary earner, that adapts to the other partner’s preferences.
As shown above, the overall effects of differences in income and education on com-
muting distances have already been in the focus of previous research. However, it has not
been analysed so far whether the known mechanisms also apply within the group of profes-
sionals. Assuming a homogeneous skill-level within this group, the influence of different
incomes on a households commuting behaviour has to be tested. Thus, the assumption is as
follows:
A3.1: A higher income of a male professional decreases the commuting distance of
his (female) partner. Reversely, the influence of a female professional’s income on
her male partner is less striking. For the entirety of workers, the results are similar
but more distinct.
As shown above, professionals are known to generally commute longer distances than reg-
ular workers. Both a higher salary and a more complex qualification profile have been iden-
tified by previous research as channels for this observation (Haas 2012; Groot etal. 2012;
Dauth and Haller 2018; Haas and Hamann 2008; Rüger and Sulak 2017). Thus, profession-
als are associated with higher earnings and longer commutes.
A household with two working spouses has to solve more complex problems regarding
joint location choice and individual commuting distances. On the one hand, both spouses
aim to maximise the households joint income. On the other hand, they value to spend time
with each other and potential children (Swärdh and Algers 2016). Moreover, non-occupa-
tional household tasks have to be performed, usually to a large share by women while men
are associated with both a higher income and longer commuting distances (Augustijn 2018;
McQuaid and Chen 2012; Kümmerling 2015; Scheiner and Holz-Rau 2017; Swärdh and
Algers 2016).
The influence of a high qualified partner on a respondent’s commuting behaviour is
likely to vary with the respondent’s qualification and income. The higher a spouse’s quali-
fication and thus expected income is, the higher is this spouse’s optimal share in the con-
tribution to the household income. Contrary, a lower educated spouse is more likely to
perform unpaid but obligatory household tasks. In case that both partners are profession-
als with high education and a high expected income, comparably long commutes can be
assumed for both spouses (Green 1997). To maintain a sufficient level of family time and
housework, it is likely that bargaining between the spouses has either equal distances and
shares of housework or separation of occupational work and household tasks, as a result,
(Hjorthol and Vågane 2014; Scheiner and Holz-Rau 2017).
Gender differences have to be considered since Swärdh and Algers (2016) found
both spouses to value women’s commuting time higher. As summarised above, previous
research suggests traditional “male breadwinner” and “female housewife” role models to
be prevalent. Thus, the resulting assumption is:
A3.2: The impact of the partner’s education on a professional’s commuting distance
is negative. The effect is more striking for female professionals. The effects are less
striking in the full sample.
The location and commuting choices of households that contain children are more com-
plex. In general, an overall effect of children on the commuting behaviour of their parents
is widely recognised (e.g. McQuaid and Chen 2012; Sultana 2005; Scheiner and Holz-Rau
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2017; Pfaff 2012; Vuk etal. 2016). It can be assumed that parents want to spend time with
their children e.g. for education and nurturing (Vuk etal. 2016) so that the opportunity
costs of commuting time increase with each child. Empirical finding (McQuaid and Chen
2012; Sayer etal. 2004; Hjorthol and Vågane 2014) suggest that this general theory is valid
for women whose commuting times decline when they have children. In contrast, men with
1 to 3 children commute longer than those without children. Gender-related differences are
usually derived from both sociological role models and economic dependencies such as
income differences that induce different mobility behaviour as shown above. Thus, these
differences might vary for different educational and occupational groups, as income, as
well as role models, differ from other groups. Böhm etal. (2016) and Sayer etal. (2004)
found occupational and educational variables evident to influence on the daily minutes
of child care in four developed countries. In Germany, both lower-skilled and non-white-
collar employed mothers and fathers spent fewer minutes with their children. The effects
turned out to be more striking for women than for men. Also for Copenhagen, the effect of
education on the priority of family time is positive (Vuk etal. 2016), so that the opportu-
nity costs of spending time with commuting increase for professionals. Hence, the assump-
tion is:
A3.3: The commuting distance of women declines with a higher number of children,
while men commute further. The effects are less pronounced for professionals.
Analysis andresults
Data cleaning
The following sections’ analyses are based on data from the German Microcensus of the
year 2012 (Forschungsdatenzentren der Statistischen Ämter des Bundes und der Länder (b)
2012), that represent the interviews of approx. 1% of the German population (Forschun-
gsdatenzentren der Statistischen Ämter des Bundes und der Länder (a) 2019). The 2012
version of the German Microcensus was chosen for the analysis since it contains a set of
questions that deal with the individual commuting behaviour. More recent datasets do not
contain these or other necessary attributes, as they are not part of the regular questionnaire.
Since this paper aims to analyse the dependencies between the commuting behaviour of
employees and their sociodemographic characteristics, several groups of the German popu-
lation are excluded from the investigation. On the occasion of a mobility survey, Nobis
etal. (2019) compared several trends in Germany concerning mobility issues and depicted
several changes over this period. First, the German population grew older on average, the
trend was primarily driven by the babyboomer generation. The share of household with an
age of 65 and above increased from 20 to 30%. Both the employment rate and the person
kilometre travelled for commuting increased by about 10%.
First, only those participants are taken into account, whose primary income is usually
based on their efforts. Further on, only the population of working age is considered, which
excludes children under the age of 15 (OECD 2020) as well as pensioners. Soldiers, vol-
unteers and farmers are excluded as well as they tend to bias either the regular commuting
patterns or the sociodemographic characteristics or both.
Since previous research unveiled differing effects for male and female workers, the vari-
ables are analysed separately for the two genders. In the German Microcensus of 2012 gen-
der is coded as a binary variable that distinct men and women. More recent surveys also
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contain an attribute for undefined gender. On this basis, the analysis is limited to conven-
tional gender concepts.
One way to assess a professional’s qualification level is to distinct occupational groups.
Similar to Alexander and Dijst (2012), this contribution focuses on higher-level profes-
sional workers. In accordance to the International Standard Classification of Occupation
(ISCO-08), those are defined as legislators, senior officials and managers (ISCO 1), profes-
sionals (ISCO 2) and technicians and associate professionals (ISCO 3). They differenti-
ate from semi-skilled white-collar occupations, semi-skilled blue-collar occupations and
elementary occupations (International Labour Office (ILO) 2012).
The respondents’ levels of education are considered due to the International Standard
Classification of Education (ISCED-97). The classes ISCED 5A (at least a 3-year theory-
based program), ISCED 5B (qualification for direct labour market entry) and ISCED 6
(advanced research programs) represent the tertiary levels of education (OECD 1999).
To cover both occupational and educational qualification, only respondents with
the described ISCED- and ISCO-classifications are considered for the professional’s
subsample.
Several questions of the German Microcensus survey are open and can be answered vol-
untarily. The participants with missing values in the analysed variables have been omitted
in the analysis.
Descriptive analysis
Table1 presents basic descriptive results on the variables selected above. It is distinguished
between all respondents and the subset of professionals to allow for some general compari-
sons. The state of research indicates various effects for men and women. Thus the results
for male and female respondents are presented separately.
As the first group of variables, the different commuting distances are tested. The micro-
census questionnaire provides 5 distance categories ranging from 0 to 50km and more.
Respondents with constantly changing workplaces are excluded from the analysis. The
results presented in Table1 are generally in line with the expectations derived from the lit-
erature above. Professionals commute longer distances than workers of the full sample and
the shares of men that commute longer distances are larger than for women in both cases.
The second group consists of variables related to the respondents’ occupation. In the
survey, income is measured in 24 nonlinear categories with an increasing range from 0€ to
18,000€ and more. To create comparable groups, approximate income quintiles are com-
puted on the base of the full sample after data cleaning. As can be seen in Table1, most of
the five income quintiles for men and women are close to a share of 20%. As expected, in
both sets the lower-income groups are dominated by women whereas men achieve compa-
rable higher incomes. This observation holds for the group of professionals, although the
women’s shares are more evenly distributed. More then half of the male professionals are
ranked in the highest income quintile.
Besides the income, previous research identified fixed-term contracts, part-time work
and supervisory or management-related tasks as possible drivers or restrictions of an
employee’s commuting behaviour. As implied by the state of research, almost all male
respondents worked full-time, while a large share of women of 40% worked part-time.
Unlike before, the shares of part- and full-time workers do not increase or decrease signifi-
cantly for the professional’s data, implying that the average weekly working hours are not
a matter of qualification. For fixed-term contracts, striking differences can be found neither
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Table 1 Number of observations and shares of the chosen categories for the full sample and professionals Source: https ://doi.org/10.21242 /12211 .2012.00.00.1.1.1, own cal-
culations
N (all) Share (all) N (prof.) Share (prof.) Description
m w m w m w m w
Distance Q1 24,936 30,119 0.24 0.33 5519 6068 0.23 0.29 Distance to work: less than 5km
Distance Q2 20,780 20,254 0.20 0.23 4462 4492 0.18 0.22 Distance to work: 5–10km
Distance Q3 32,212 26,259 0.32 0.29 7227 5906 0.29 0.29 Distance to work: 10–25km
Distance Q4 17,564 10,621 0.17 0.12 4833 3115 0.20 0.15 Distance to work: 25–50km
Distance Q5 6912 2829 0.07 0.03 2528 1055 0.10 0.05 Distance to work: more than 50km
Income Q1 13,302 33,070 0.13 0.37 522 2491 0.02 0.12 Average wage per month: less than 1100€
Income Q2 18,660 22,066 0.18 0.25 1248 3946 0.05 0.19 Average wage per month: 1100–1500€
Income Q3 25,280 18,040 0.25 0.2 3180 5234 0.13 0.25 Average wage per month: 1500–2000€
Income Q4 21,860 9829 0.21 0.11 5866 4510 0.24 0.22 Average wage per month: 2000–2600€
Income Q5 23,302 7077 0.23 0.08 13,763 4455 0.56 0.22 Average wage per month: more than 2600€
Fixed-term contract 11,487 10,972 0.11 0.12 1801 2295 0.07 0.11
Permanent contract 90,917 79,110 0.89 0.88 22,778 18,341 0.93 0.89
Part-time 5169 35,487 0.05 0.39 1100 6362 0.05 0.31
Full-time 97,235 54,595 0.95 0.61 23,479 14,274 0.96 0.69
Supervisor/manager 25,369 13,549 0.25 0.15 12,324 5873 0.50 0.29
Regular employee 77,035 76,533 0.75 0.85 12,255 14,763 0.50 0.72
West Germany 82,228 69,471 0.80 0.77 20,101 14,552 0.82 0.71
East Germany 16,797 16,728 0.16 0.19 3389 4741 0.14 0.23
Berlin 3379 3883 0.03 0.04 1089 1343 0.04 0.07
Income Q1 (partner) 46,593 27,056 0.62 0.44 9263 2254 0.50 0.17 Partner’s average wage per month: less than 1100€
Income Q2 (partner) 12,366 14,473 0.17 0.23 2909 2744 0.16 0.20 Partner’s average wage per month: 1100–1500€
Income Q3 (partner) 8533 10,704 0.11 0.17 2636 3244 0.14 0.24 Partner’s average wage per month: 1500–2000€
Income Q4 (partner) 4363 5728 0.06 0.09 1823 2762 0.10 0.20 Partner’s average wage per month: 2000–2600€
Income Q5 (partner) 3038 3864 0.04 0.06 1753 2665 0.10 0.20 Partner’s average wage per month: more than 2600€
High education (partner) 17,092 16,355 0.23 0.26 9383 13,249 0.51 0.97 Partner’s ISCED-level is 5 or 6
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Table 1 (continued)
N (all) Share (all) N (prof.) Share (prof.) Description
m w m w m w m w
Low or regular education (partner) 57,763 45,451 0.77 0.74 9001 400 0.49 0.03 Partner’s ISCED-level is below 5
No child 68,354 62,436 0.67 0.69 15,645 14,243 0.64 0.69
One child 17,369 16,155 0.17 0.18 3982 3405 0.16 0.17
Two children 13,014 9597 0.13 0.11 3928 2481 0.16 0.12
Three and more 3586 1830 0.04 0.02 1022 499 0.04 0.02
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for gender nor for qualification differences. Roughly 90% of all men and women work in
permanent contracts.
As a third group of variables, the federal state of residence is analysed to account for
regional differences. In this case, Germany is divided into East and West, while Berlin as a
metropolis is considered separately since its economic structure differs from the surround-
ing rural areas (Haas and Hamann 2008; Haas 2012; Granato etal. 2009). In both groups,
approximately 80% of the respondents stated to live in West Germany, whereas about 20%
are from East Germany and about 4% reside Berlin, which in general fits official demo-
graphic data. However, the distribution of female professional respondents deviate from the
official data and is slightly but disproportionately high for Berlin and East Germany. The
available data do not allow spatial analyses on a small scale. Thus, only the effects of mac-
roeconomic regions are taken into account.
The last group accounts for the household structure and analyses the dependence of
commuting and the spouse’s characteristics. Respondents without a partner are not con-
sidered in the partner-related analysis. Initially, the partner’s income group is measured in
the income quintiles as used above. In the full sample 62% of men’s spouses received an
average wage of 1100€ per month and about 80% of the partners belonged to the lowest
two income groups. Although the women’s values are more evenly distributed, but point
in the same direction. This decreasing share within the income groups is partly due to the
lower availability of high-income potential partners as depicted in the results for the second
variable group. However, the result also supports the former observations and theories of
previous research on gender-specific differences in socioeconomic factors such as part-time
work or role models. These dynamics appear to hold for the group of professional men,
although their spouses tend to receive a higher wage, as the lower-income groups are not as
strong as those in the complete sample. The values of female professionals are distributed
approximately even so that women with higher skills and education form the group with
the (on average) best-earning partners.
Since the underpinning data are of 1 year, the effects of long-term developments such as
the ageing of children or employees or other life-cycle events are not considered here.
To measure the partner’s qualification level, the microcensus data provide the ISCED-
classification as used above. As for the selection of the professional’s subsample, the
ISCED 5 and 6 classes are used to separate the respondent’s partners with higher education
from the full sample. As Table1 depicts, gender difference on the partner’s education is not
prevalent for both men and women, as long as the full sample is considered. Approximately
one-quarter of the partners are highly qualified due to the ISCED-classification. When
switching to the professionals, this parity disappears, as almost all (97%) of the female
respondents stated to have a partner with higher education, whereas the male respondents
declare only about 50% of their partners as highly educated. This tendency of professional
women to couple with higher-skilled partners goes along with the general observations on
the partners’ income.
Finally, the number of children per family is measured in four categories ranging from 0
to 3 or more children. As expected, men and women have approximately the same number
of children. Also, there are no significant differences between the educational groups.
Ordered logit regression
To estimate the influence of the variables introduced in Table1 on the commuting dis-
tance, four ordered logistic regression models are calculated. Their results are presented in
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Table 2 Ordered logit regression on the microcensus data after cleaning Source: https ://doi.org/10.21242 /12211 .2012.00.00.1.1.1, own calculations
Dependent variable: Exp. influ-
ence
Model 1 (all) Model 2 (all) Exp. influ-
ence
Model 3 (prof.) Model 4 (prof.)
Commuting distance m w m w m w m w m w m w
Income Q2 + + 0.266*** 0.364*** 0.179*** 0.420*** + + − 0.064 0.130*** − 0.137 0.312***
(0.022) (0.046) (0.028) (0.033) (0.097) (0.046) (0.093) (0.093)
Income Q3 + + 0.399*** 0.533** 0.345*** 0.541*** + + 0.128 0.222** 0.009 0.422***
(0.022) (0.018) (0.027) (0.040) (0.090) (0.046) (0.116) (0.106)
Income Q4 + + 0.601*** 0.693*** 0.562*** 0.469*** + + 0.264*** 0.331*** 0.139 0.380***
(0.023) (0.048) (0.028) (0.049) (0.089) (0.048) (0.114) (0.115)
Income Q5 + + 0.822*** 0.760*** 0.812*** 0.556*** + + 0.405*** 0.418*** 0.261** 0.444***
(0.024) (0.026) (0.029) (0.051) (0.088) (0.050) (0.113) (0.121)
Fixed-term contract + + 0.091*** 0.205*** 0.161*** 0.302*** + + − 0.466*** 0.034 − 0.378*** 0.168***
(0.020) (0.019) (0.025) (0.025) (0.047) (0.041) (0.059) (0.055)
Part-time − − − 0.419*** − 0.246*** − 0.451*** − 0.311*** − − − 0.324*** − 0.022 − 0.299*** − 0.096**
(0.027) (0.014) (0.035) (0.018) (0.060) (0.030) (0.073) (0.039)
Supervisor/manager + + 0.020 0.004 0.018 − 0.025 + + − 0.001 0.011 − 0.045* − 0.009
(0.014) (0.018) (0.016) (0.022) (0.023) (0.028) (0.027) (0.035)
East Germany o o 0.188*** 0.026* 0.185*** − 0.004 − − − 0.059* − 0.162*** − 0.087** − 0.209***
(0.016) (0.016) (0.019) (0.019) (0.035) (0.031) (0.041) (0.037)
Berlin − − − 0.052* 0.104*** − 0.095** 0.043 − − − 0.372*** − 0.144*** − 0.466*** − 0.258***
(0.030) (0.028) (0.039) (0.038) (0.052) (0.048) (0.067) (0.066)
Income Q2 (partner) + o 0.075*** 0.015 o o 0.030 − 0.109
(0.018) (0.032) (0.038) (0.093)
Income Q3 (partner) + − 0.105*** 0.103*** o o 0.024*** − 0.066
(0.022) (0.040) (0.041) (0.106)
Income Q4 (partner) o − 0.149*** 0.339*** o − 0.154*** 0.093***
(0.030) (0.050) (0.048) (0.114)
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Table 2 (continued)
Dependent variable: Exp. influ-
ence
Model 1 (all) Model 2 (all) Exp. influ-
ence
Model 3 (prof.) Model 4 (prof.)
Commuting distance m w m w m w m w m w m w
Income Q5 (partner) o − 0.049 0.261*** o − 0.080 0.096
(0.036) (0.056) (0.050) (0.123)
High education (partner) o − − 0.018 0.041** o − − 0.161*** − 0.807***
(0.017) (0.019) (0.029) (0.106)
One child o − 0.023 0.124*** o o 0.105*** 0.102**
(0.017) (0.019) (0.034) (0.041)
Two children + − 0.014 0.119*** o − 0.105*** 0.084*
(0.018) (0.23) (0.035) (0.045)
Three and more + − − 0.103*** − 0.183*** o − 0.065 − 0.114
(0.032) (0.047) (0.059) (0.088)
Observations 102,404 90,082 74,855 61,806 24,579 20,636 18,384 13,649
Residual Deviance 307,777.60 252,944.87 225,652.10 172,839.72 76,176.97 61,213.92 56,991.83 40,440.93
Reference categories: income Q1; permanent contract; full-time work; regular employee; West Germany; income Q1 (partner); low or regular education; no child
Model 1: full sample, Model 2: full sample (with partner), Model 3: professionals, Model 4: professionals (with partner); after data cleaning
*
p
<
0.1
; **
p
<
0.05
; ***
p
<
0.01
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Table2 and show the expected influence of the variables in comparison to a suitable refer-
ence category and are derived from the literature review above.
Logistic regression approaches are suitable to estimate models for ordinal variables.
Since the German Microcensus provides categorical information on a respondent’s com-
muting distance
d
,
Yi
ranges from 0 to 50+km. Following the work of Fahrmeir etal.
(2013) and Agresti (2010), an ordered logistic regression approach is applied to the data to
estimate the effects of the sociodemographic variables on the commuting distances.
where
r∈{1, ..., 5}
denotes the category of distance to work for respondent
i
. The prob-
ability that a respondent is in a certain category or lower can be written as
with F being a logistic cumulative distribution function of
𝜆r∈{0 km, 50 km}
as the corre-
sponding categories’ threshold and
x′
i𝜷
as the product of covariates and a parameter vector
without an intercept. A regression on this probability uses
𝜆r
and
𝜷
as parameters and
xi
as
regressors.
When applying a logit transformation to Eq.1, the cumulative logits can be written as
The log odds defined by Eq.2 are computed in R (R Core Team 2019) with the polr-func-
tion of the package MASS (Venables and Ripley 2002). Unlike in Fahrmeir etal. (2013)
and Agresti (2010), polr defines log odds as
for better interpretation. By that, every result depicted in Table2 can be interpreted as the
logarithmic odds for being in a higher commuting category. Since for each log odds r,
log(r)
<
1
for
r<0
and
log(r)
>
1
for
r>0
, the direction of the estimated influence can be
assessed without further transforming.
For each of the estimated models, the log odds and standard errors are presented for
different samples. The first model contains all male and female respondents of the German
Microcensus after data cleaning, whereas the second model is limited to respondents living
in a partnership to control for household effects.
The third and fourth model are set up alike but are based on the subsample of profes-
sional respondents due to the ISCED and ISCO classifications as introduced above. Thus,
Y
i=r=
⎧
⎪
⎪
⎨
⎪
⎪
⎩
1, if 0 km ≤di<5 km
2, if 5 km ≤di<10 km
3, if 10 km ≤di<
25 km
4, if 25 km ≤di<
50 km
5, if 50 km ≤di<∞,
(1)
P
(Y
i
≤r)=F(𝜆
r
+x
′
i
𝜷)
,
(2)
logit
[P(Yi≤r)] = log
P(Y≤r)
1−P(Y≤r
)
=log P(Yi≤r)
P(Yi>r)
=𝜆r+x′
i
𝜷.
(3)
logit
[P(Y≤r)] = 𝜆
r
−x
′
𝜷
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the number of observations in the different samples ranges from about 100,000 in the male
column of model 1 down to 14,000 in the female column of model 4.
Occupational implications
As shown in the state of research section, income has already been identified as one of the
most important influences on an individual’s commuting behaviour by previous research.
As Table2 implies, being in a higher income group is in general significantly and posi-
tively related to a person’s commuting distances and holds for all samples with only a few
exceptions. For both columns of model 1, the coefficients for the income increase steadily
with a rising wage, and are all positive and highly significant. The effects tend to be rela-
tively higher for female employees, except in the highest category. For men in the highest
income group, the odds for being in a higher commuting class than the reference group are
e0.822 =2.28
.
When considering the family constraints as depicted in model 2, most income-related
coefficients decrease. Men of medium and high-income groups appear not to be affected
distinctively, whereas most coefficients for high-income females lose impact (although not
their significance). Oppositely, the coefficients for medium or lower female income slightly
increase in comparison to the full sample.
For the professionals, the effects of an increasing income appear not to be as distinct
as for the entirety of respondents. In model 3 and 4, the differences in the commuting dis-
tances between the reference group and male professionals with a medium or lower income
lose their significance and are less distinct or (although insignificant) even negative. Add-
ing the set of family constraints leads to an even more declining relation, leaving only the
highest income group with a significant difference to the reference group. In comparison
to their male counterparts, the income effect is less diverging for female professionals of
model 3, although the coefficients are comparably smaller than in the sub-sample. Unlike
in the full sample and for male respondents, adding family constraints results in higher
distances in every class. Especially the low and medium-income classes are affected, with
coefficients rising from 0.130 to 0.312 and 0.222 to 0.422 respectively. However, since
some groups contain only comparably few respondents (Table 1), the results might be
biased. Moreover, multicollinearity with variables such as part-time work might be an
issue.
Next to income, the temporal limitation of employment contracts has been assumed to
exert an influence on an individual’s commuting distance. As Table1 depicts, only around
10% of every group hold fixed-term contracts whereas about 90% of all respondents (after
cleaning) are permanently employed.
Turning to the full-sample models of Table2, the assumption of a positive relation-
ship between a fixed-term contract and the commuting distance can be confirmed, with
more distinct effects for women than for men. The results are in line with the expectations
derived from the literature and all coefficients are significant on a 99% level. Model 3 dem-
onstrates that a fixed-term employed male professional’s odds for being in a lower commut-
ing distance category than his counterpart with a permanent contract are
1−e0.466 =0.37
,
thus 37% higher. The female coefficient is close to zero and not significant on a sufficient
level.
Under family constrains (model 4), the coefficients for female professionals gain in
both significance and relevance. When being part of a household, a fixed-term working
female professional’s odds to commute a longer distance than her permanently employed
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counterpart, are
e0.168 =1.18
, thus 18%. Under family constraints, the male coefficient
remains distinctively negative.
In contrast to fixed-term contracts, part-time work is assumed to exert an overall nega-
tive influence on the commuting distances. While fixed-term contracts are associated with
a full salary, part-time workers usually receive a reduced wage that makes commuting
comparably more expensive and stressful.
The results of Table2 are in line with these expectations. The coefficients are nega-
tive for all sub-samples and thus indicate a negative influence of part-time work on the
commuting distance. As a tendency, the effects are comparably less striking for the pro-
fessionals. Especially for female professionals, the influence dilutes in both power and
significance.
In general, the estimated impacts of the occupational variables on the odds for com-
muting longer distances are in line with the expectations and fit several assumptions. First,
assumption A 1.1 stated a correlation between higher incomes and longer commuting dis-
tances and can be confirmed at this point. The results of model 1 and 2 are in line with the
expectations derived from the literature and assuming a positive correlation of income and
commuting distance with stronger results for men and women. Adding family constraints
results in shorter commuting distances also for men but especially for women with higher
income, which is in line with traditional role models of “male breadwinners” and “female
housewives” (Scheiner and Holz-Rau 2017; Augustijn 2018; Surprenant-Legault et al.
2013; Sultana 2005). Concerning previous research, the effects for male and female work-
ers are considered separately. As described, assumption A 1.1 is valid for female profes-
sionals in the full sample. It must be limited only slightly when adding family constraints,
as the corresponding coefficients do not increase linearly. For male professionals, assump-
tion A 1.1 can neither be accepted nor rejected explicitly. On the one hand, the coefficients
at least for medium or higher income groups rise steadily. On the other hand, most results
are not significant on a sufficient level, particularly in comparison to the full sample and
the female counterparts. Compared with the full workforce, the effects of income changes
dilute for professionals.
Assumption A 1.2 stated that professionals with fixed-term contracts commute longer
distances than those with permanent contracts. Both rather lower costs for commuting than
for relocation and a higher acceptance for commuting stress due to the temporal limita-
tion were suggested to form the underpinning mechanism of a higher willingness to com-
mute. As the results show, the assumption can be accepted for the full-sampled models and
rejected for male professionals. The case of female professionals appears to be more com-
plex, as at least under family constraints a positive and significant influence is found. How-
ever, all coefficients of model 2 and 4 are higher than their counterparts. As higher (oppor-
tunity) costs of relocation can be assumed for households than for singles, this observation
gives support to the view on commuting as an avoidance-behaviour for relocation (Pfaff
2012; Rüger and Sulak 2017; Scheiner 2016). Nevertheless, the negative coefficients for
male professionals remain surprising, since a positive influence was expected. One cor-
responding observation based on the descriptive results (Table1) is that a large share of
male professionals receives comparably high incomes that come along with longer com-
muting distances. Additionally, male professionals represent the subsample with the lowest
share of fixed-term contracts. Since both the literature and descriptive statistics suggest
an aversion to this kind of employment, there is a row of viable explanations. First, male
workers could show a low willingness to commute and thus accept fixed-term contracts
close to their place of residence. Second, these workers might show an extraordinary high
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willingness to relocate their residence for a comparably short period to shorten the daily
commuting distance. The less negative (but still distinct) coefficient for male partners liv-
ing in a household give support to this approach since the relocation of a household can
be assumed to be more costly and difficult due to bargaining, more restrictions and higher
(opportunity) costs (Pfaff 2012; Wagner and Mulder 2015; Van Ommeren etal. 1999).
Third, Table1 suggests gender-related differences in the reasons for seeking (or finding) no
permanent employment with an influence on commuting behaviour.
Assumption A 1.3 can be accepted in all models. The influence of part-time work on the
commuting distance is negative in all samples, although the effects for female profession-
als are not significant until family constraints are added. In general, the estimates for the
professional’s sub-samples are closer to zero. This result might be channelled by a higher
income of better-educated employees. As Table1 depicts, the incomes of both male and
female professionals are higher than in the full sample, whereby men benefit most from
higher education. Since commuting costs can be assumed to be equal for every employee,
professionals have to spend a lower share of their income to cover a similar distance.
As the corresponding row depicts, assumption A 1.4 must be rejected for all models
since all coefficients are positive but close to zero and without significance. As an excep-
tion and contrary to McQuaid and Chen (2012), male professionals in managerial occupa-
tions are found to have a slightly higher chance to commute a shorter distance than other
professionals. The finding gives support to Sultana (2005), who unexpectedly found man-
agers in the Atlanta metropolitan area to commute comparably short distances.
Regional divergence
The second set of assumptions refers to the impact of regions within Germany as the state
of research suggests differences in the commuting behaviour of East and West German
professionals.
Indeed, the models 3 and 4 validate that especially for East German female professionals
the odds are considerably high to be in a lower commuting class than West German female
professionals. In comparison, in the full sample, the estimated coefficients for the females
lose their significance and are close to zero, whereas the odds for men to be in a higher
commuting class than West Germans are around 20% in both models. Berlin is separated
from the analysis as it is Germany’s most populated city and a centre of economic activity.
Hence it is likely that the city’s worker exerts a different commuting behaviour than their
counterparts in the surrounding federal states of East Germany. Indeed, this presumption
holds at least for professionals and men from the full-sample, who are more likely to com-
mute shorter distances whereas the effect for women is the opposite.
In general, assumption A 2.1 is valid and especially matches the results for female pro-
fessionals. However, the results must be interpreted with caution since there is a row of
possible biases. First, living and working in a certain region could be correlated with vari-
ables of the occupational set, since economic differences between East and West Germany
and Berlin are prevalent and known (Bogai etal. 2014; Haas and Hamann 2008; Granato
etal. 2009). Moreover, these differences could also account for the quality of infrastructure
or the balance of rural and urban areas, which previous research suggests to be important
drivers of commuting (Kohl 2014; Haas and Hamann 2008). The finding, that commuting
connects rural areas, suburbs and the urban core of a region, as well as economic strong
and weak areas, is in line with the literature (Einig and Pütz 2007; Winkelmann 2010). The
underpinning economic dynamics point in the same direction as the findings from other
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research areas e.g. in North America (Shearmur and Motte 2009; Sultana 2002), although
most of the geographic models applied in the literature are of smaller scale and thus of
these spatial characteristics.
Household dynamics
Turning to the household characteristics, a respondent’s partner’s income is considered to
influence the respondent’s commuting distance as it is part of the households joint optimi-
sation problem. As shown above, previous research suggests the traditional “male bread-
winner” and “female housewife” model as the major explanation for gender differences in
commuting times. Thus, the direction of the influence is expected to differ between male
and female respondents.
For the full sample, model 2 estimates an overall positive influence of the partner’s
income on an individuals commuting distance. For male respondents, a partner’s income
within the quintiles 3 and 4 exerts the strongest influence on the men’s’ commuting dis-
tance with the odds for being in a higher commuting category being 11% to 16%. In com-
parison, women’s commuting distances are most stimulated by a partners income within
the quintiles 4 and 5, with the odds being 30% resp. 40%.
Assumption A 3.2 states a negative influence of the partner’s education on an individu-
als commuting distance with males being less affected than females.
Again, the estimated coefficients differ by both gender and educational status of the
respondent. In the full sample, only the female’s odds for commuting longer distances are
significant, although with odds of only
e0.041 =1.04
, thus 4%. However, the men’s com-
muting distance is not stimulated significantly by the spouses’ educational level.
In comparison, a significant and strong influence of the partner’s education on the own
commuting distance is observed for the professionals. As expected, the effect is much
stronger for females with the odds being
1−e−0.807 =0.55
, thus 55% to be in a lower com-
muting class than a counterpart with a less educated partner.
Finally, the influence of children on the commuting distance is tested. As discussed
above, children, on the one hand, add further restrictions to the households joint optimisa-
tion problem, that can be assumed to cause longer commuting distances or labour division
within the household. Moreover, additional household members also increase the costs of
relocation. On the other hand, previous research suggests a negative impact of children on
the commuting distance at least for women. Usually, these observations are explained with
labour division based on role models and a higher demand for family-time (Ruppenthal and
Lück 1999).
The estimated coefficients are not in line with the expectations. In the full sample, the
odds of women with one or two children to commute longer distances than women without
children are about 13%. Only women with three or more children are likely to commute
shorter distances. For men, the coefficients for one and two children are not significant and
close to zero, while the effect of having three and more children is similar to their female
counterparts. In comparison to the full-sample model, the effect of children on the com-
muting distance dilutes for female professionals, since both significance and coefficients
decline. In contrast, the odds of male professionals with one or to children to commute
longer distances are comparably higher and significant on a 99% level.
In sum, the household variables can be confirmed to influence the respondents commut-
ing distance, although not all results meet the expectations.
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Initially, assumption A 3.1 can neither be accepted nor rejected. On the one hand, espe-
cially the estimates for female professionals do not meet the expectations since they do not
decrease with an increasing income of the partner. However, the overall influence of the
partner’s income dilutes when turning from the full sample to the professional’s subset.
In general, the effect is visible for male and female as well as professional and other
employees but reveals some interesting differences. Especially the results for female
respondents are surprising at this point, as a negative influence of a woman’s partner’s
income on her commuting distance was expected. The data cannot confirm the observa-
tions of e.g. Ruppenthal and Lück (1999), Scheiner (2016) or Hjorthol and Vågane (2014)
regarding the expected influences of traditional role models and the corresponding divi-
sion of tasks between spouses. For both male and female professionals, the coefficients are
closer to zero and lose significance compared to the full-sample estimates. This distinction
suggests a decreasing impact of the partner’s income on a respondent’s commuting dis-
tance with an increasing educational level of the respondent. Obviously, other characteris-
tics gain in importance.
Sultana (2005) described fundamental differences between the commuting patterns in
American (dual-earner households commutes are slightly longer than single-earner com-
mutes) and European (no significant differences or mixed effects) cities and refers on con-
tributions around the year 2000. Sultana (2005) themselves concluded for the metropolitan
area of Atlanta, that the commute times of dual-earner households are comparably shorter.
For Germany, an employee’s commuting distance is likely to increase, once the spouse is
not in the lowest income category (or in the case of female professionals, not below 2000€
per month), which gives support to the theory of more complex location choices and thus
longer commutes of dual-earner households. Considering the previous findings and the
results of this contribution, these differences might be ripe for a more detailed re-examina-
tion. However, they are not necessarily outdated, as different study-designs could be a bias.
Nevertheless, these findings might be limited due to two constraints. Note that a consid-
erable share of men’s partners (usually women) are in the lowest income class, regardless
of the men’s own educational and occupational status (Table1). The partners of female
professionals (usually men) are roughly equally distributed along with the income groups,
whereas about 67% of their counterpart in the full-sample are in income quintile 1 or 2.
Moreover, a correlation of the income variable with both other variables such as education
and the partner selection is possible and could bias the result at this point.
The educational status of the partner was found to decrease the commuting distance
of professionals. Especially female professionals were found to commute shorter distance
when their partner is well-educated. Although this finding is generally in line with the lit-
erature (Scheiner 2016), the results are much higher than for any other group. Previous
research suggests variables such as a valuation of family-time to reduce commuting dis-
tances within households (Swärdh and Algers 2016; Vuk etal. 2016). Moreover, a general
affinity for short commutes due to costs and negative health effects is apparent. To explain
the striking coefficient of female professionals, traditional role-models fits well to the
observed differences within the group of professionals. Traditional labour division within
the household could explain both a comparably low income and a high share of part-time
work for female professionals (Table1). One viable interpretation for the small or slightly
negative coefficients of the respondents in the full-sample builds on economic restrictions.
In the models 3 and 4 both or at least one partner is a professional, while in the mod-
els 1 and 2 no or maximal one partner belongs to the highly skilled and educated group
of workers. Thus, as the expected household income within the full-samples is lower, a
longer commuting distance of both partners might be necessary to maintain a sufficient
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level of the households cumulative income. Nevertheless, this interpretation is not suffi-
ciently backed by the corresponding coefficients, that tend to increase with a higher part-
ners’ income. Again, a correlation of the income variable with the educational level or
partner selection patterns is possible and could bias the estimates.
However, since couples with two highly qualified spouses have high odds to commute
shorter distances than the reference group or the respondents of the full sample and the
effects are less distinct for men, assumption A 3.2 is validated.
Finally, coefficients for the number of children were estimated and found to influence
the commuting distance for certain sub-samples. Based on the state of research, contrary
effects on the gender groups were presumed, with men commuting longer and women
shorter distances with every additional child (McQuaid and Chen 2012; Motte-Baumvol
etal. 2017; Hjorthol and Vågane 2014). To assess the differences between the entirety of
workers and the professional’s subsample, assumption A 3.3 states less significant impacts
of children on the commuting distances.
Focusing on the respondents of the full-sample models, the positive odds for longer
commutes of women with children than for women without children are surprising since
they differ from most of the previous findings and suggestions (McQuaid and Chen 2012;
Hjorthol and Vågane 2014; Sultana 2005). Only that finding, that women with three and
more children (and thus numerous additional restrictions) travel shorter distances, is in line
with the expectations. The results for males do generally not contradict the expectations,
but the coefficients are less positive than suggested by the literature. Apparently, longer
commutes are accepted by parents, as long as the additional burden caused by childcare is
not too high.
An economic explanation arises from the higher relocation cost of a household with
additional members as described above. Since the needs of a child, such as a suitable
kindergarten, school or neighbourhood, have to be considered in the decision for a resi-
dential location, the commuting distances increase, whereas the demand for family-time
increases at the same time. A well-known reaction to this more complicated problem is
that one partner accepts a new (possibly part-time) job in the vicinity with both a probably
worse qualification-match and a lower income, whereas the partner with a higher willing-
ness to commute keeps working full-time in a longer distance. Both empirical findings on
this willingness to commute and traditional role models suggest that usually, women tend
to reduce their commuting distance in this kind of situation (Hjorthol and Vågane 2014;
McQuaid and Chen 2012; Auspurg and Schönholzer 2013; Sultana 2005; Motte-Baumvol
etal. 2017). The respondents of the professional’s sub-sample fit better to these previous
findings since for highly educated fathers positive and for highly educated mothers nega-
tive or at least comparably smaller coefficients were estimated.
Nevertheless, from the first child on, there is a decline of the men’s and women’s coef-
ficients with any additional child, with the male’s being usually larger than the female’s.
Thus, the observed impacts fit the assumption A 3.3 sufficiently. The increasing commut-
ing distances of men are in line with the literature as presented above. Thus, assumption A
3.3 can be accepted, but the results dilute in comparison to previous investigations. Further
on, the results for the influence of three children have to be treated with caution, since the
underpinning group of respondents is comparable small and accounts for only 2% of the
sample size.
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Conclusion
This analysis aims to complement the broad state of research on commuting with the
perspective of professional workers. Contributions such as Wrede (2013), Hjorthol and
Vågane (2014), Sultana (2002, 2005) and Groot etal. (2012) considered the occupational
or educational status and provide initial suggestions for certain effects for highly educated
workers. The preceding analysis extends previous and more general findings on the com-
muting of German professionals since they are found to commute longer distances than
other employees (Nobis and Kuhnimhof 2018; Haas 2012; Pfaff 2012). In both educational
groups, men commute longer distances than women. Gender-differences in the other vari-
ables such as part-time work or income are in line with the extensive body of international
literature as described above.
The main finding on professionals’ commuting behaviour is that individual occupational
variables are still of importance, but their overall influence dilutes in comparison to other
groups. Especially income, which is used by classical economic literature as the major
explanation for commuting decisions, reveals a comparatively small effect on profession-
als’ commuting distances.
Instead, the shifts in the household-coefficients indicate a higher preference for family
time instead of commuting time in relative terms. As suggested by previous research (e.g.
Swärdh and Algers 2016; Vuk etal. 2016; Hjorthol and Vågane 2014; Scheiner and Holz-
Rau 2017; Sultana 2005 or Groot etal. 2012), the impact of the partners’ characteristics on
the commuting distance is determined by gender differences. Within a household, a shift
in a woman’s educational and occupational status makes her commuting behaviour less
dependent on her children but rather dependent on her husband’s economic success. The
difference of male professionals to their counterparts with lower education remains ambig-
uous. Nevertheless, with increasing occupational and educational status, fathers commute
longer distances.
However, the results have to be treated with appropriate caution due to some limitations
of the applied methods. First, the “traditional” variables show an overall trend towards
less explanatory power for the variation in the professional’s commuting distances. Thus,
a general bias of the estimated coefficients due to an omitted variable with explanatory
content is conceivable. Second, several coefficients of the models may be somehow biased
by multicollinearity. For example, several variables such as education, fixed-term contracts
or part-time work and gender are likely to be correlated with at least the income variable.
Table1 and the regression results give support to this apprehension. Moreover, the char-
acteristics are not distributed evenly along the categories and several very small groups
(such as male professionals with a very low income) potentially bias the estimates. Finally,
according to Fahrmeir etal. (2013), the covariates of an ordered logit regression model
must not be dependent on the response variable. Although the phenomenon of commuting
can mostly be treated as a reaction to the variables analysed above, interdependencies and
reversed causality e.g. in the case of income and the commuting distance cannot be ruled
out entirely.
Due to limitations in the available data and regulations on privacy and data protec-
tion, two promising approaches, in particular, were not carried out. First, the integration
of smaller scaled geographical data in the present framework yields great potential for
improving model quality. Contributions such as Horner etal. (2015), Schleith etal. (2016),
Sultana (2005) or Korsu and Le Néchet (2017) used such data to determine the influence of
small-scale conditions on commuting behaviours and rely on quantitative spatial concepts
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such as excess commuting. Groot etal. (2012), Hjorthol and Vågane (2014), Weber and
Sultana (2008), Shearmur and Motte (2009) or Wrede (2013) stated a spatial sorting of
workers by their qualification, due to the economic or social attributes of a region, city or
neighbourhood. Better-educated workers are usually found to commute towards and live
in high-income areas with a high population density in almost every analysed country or
region.
The comparison of eastern and western federal states in Germany allows checking for
the overall influences of e.g. economic activity and infrastructure on a macro-scale. Sepa-
rating Berlin from the analysis gives only a very rough idea of disparities of urban and
rural environments and their impact on residence- and workplace-choice and commuting.
By refining the present approach, further research should analyse residence-, workplace-
and mode-choices in order to untangle the interdependences between various social, pro-
fessional and economic groups.
Such approaches already turned out to be useful in explaining general occupational
mobility and are likely to extend the explanatory power of the findings beyond the sociode-
mographics also for professionals (Wrede 2013; Scheiner 2016; Zarabi etal. 2019; Groot
etal. 2012). Especially for case studies on the commuting of employees within a certain
region, city or metropolis, specific knowledge of the underpinning geography is indispen-
sable (Sultana 2005; Surprenant-Legault etal. 2013; Shearmur and Motte 2009; Scheiner
2016; Schleith etal. 2019).
Second, a time series analysis of the microcensus data over several years could grant
insights into the long run developments of mobility and changes in the explanatory soci-
odemographic variables on an individual and aggregated level. Such approaches provide a
sound foundation for long term policy decisions on necessary services and infrastructure.
Moreover, a consideration of the different stages in individuals’ life cycles, as applied in
Beige and Axhausen (2012) or Zarabi etal. (2019), might address deeper and more fun-
damental connections between socioeconomic characteristics and the corresponding com-
muting distances, since these variables are likely to change over time.
The results of this analysis provide both the basis for further research regarding the
deep determinants of professionals’ commuting behaviour and support for a row of pol-
icy decisions. Many regions not only in Germany or Europe suffer from a brain-drain and
a general competition for skilled labour and economic prosperity with their neighbours.
A deep understanding of the determinants of occupational mobility is necessary to effi-
ciently address the causes of professionals’ spatial movements. Since professionals are
known to differ in their mode choice from other workers (Groot etal. 2012; Habib 2014),
the development of appropriate modern mobility concepts that target the professional’s
needs and favours could prove a beneficial mean for both, regional development and green
transportation.
Acknowledgements Open access funding provided by Projekt DEAL.
Author Contributions MK: Data curation, methodology, formal analysis, investigation, software, validation,
writing—original draft. EM: Conceptualization, data curation, project administration, resources, method-
ology, supervision, investigation, writing—review and editing. JL: Resources, supervision, methodology,
funding, writing—review and editing. JS: Conceptualization, data curation, funding acquisition, methodol-
ogy, resources, supervision, validation, writing—review and editing.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of interest.
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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
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References
Agresti, A.: Analysis of Ordinal Categorical Data. Wiley Series in Probability and Statistics, 2nd edn.
Wiley, Hoboken (2010). https ://doi.org/10.1002/97804 70594 001
Aguilera, A.: Growth in commuting distances in French polycentric metropolitan areas: Paris, Lyon and
Marseille. Urban Stud. 42(9), 1537–1547 (2005). https ://doi.org/10.1080/00420 98050 01853 89
Alexander, B., Dijst, M.: Professional workers @ work: importance of work activities for electronic and
face-to-face communications in the Netherlands. Transportation 39(5), 919–940 (2012). https ://doi.
org/10.1007/s1111 6-01294 00-2
Augustijn, L.: Berufsbedingte Pendelmobilität, Geschlecht und Stress. In: Duisburger Beiträge zur soziolo-
gischen Forschung (2018)
Auspurg, K., Schönholzer, T.: An Heim und Herd gebunden? Zum Einfluss von Pendelstrecken auf
geschlechtsspezifische Lohnunterschiede. Zeitschrift für Soziologie 42(3), 138–156 (2013)
Barker, L., Connolly, D.: Long distance commuting in Scotland: Scottish Household Survey/Transport
Research Planning Group topic report. Transport research series (Scotland. Social Research). Edin-
burgh: Scottish Executive Social Research (2006)
Beige, S., Axhausen, K.W.: Interdependencies between turning points in life and long-term mobility deci-
sions. Transportation 39(4), 857–872 (2012). https ://doi.org/10.1007/s1111 6-012-9404-y
Bogai, D., Buch, T., Seibert, H.: Arbeitsmarktchancen von Geringqualifizierten: Kaum eine Region bietet
genügend einfache Jobs. In: IAB-Kurzbericht, (11/2014) (2014)
Böhm, M., Franz, P., Matthiesen, S.: Studierende Eltern. Journal für Psychologie 24(1), 125–156 (2016)
Borck, R., Wrede, M.: Subsidies for intracity and intercity commuting. In: CESifo Working Paper, 2321
(2006)
Bosworth, G., Venhorst, V.: Economic linkages between urban and rural regions: what’s in it for the rural?
Reg. Stud. 52(8), 1075–1085 (2018). https ://doi.org/10.1080/00343 404.2017.13398 68
Brautzsch, H.-U.: Pendlerströme führen zur regionalen Angleichung bei Beschäftigung. Wirtschaft im Wan-
del 23(4), 69–74 (2017)
Buliung, R.N., Soltys, K., Bui, R., Habel, C., Lanyon, R.: Catching a ride on the information super-highway:
toward an understanding of internet-based carpool formation and use. Transportation 37(6), 849–873
(2010). https ://doi.org/10.1007/s1111 6-010-9266-0
Champion, T.: Urban–rural differences in commuting in England: a challenge to the rural sustainability
agenda? Plan. Pract. Res. 24(2), 161–183 (2009). https ://doi.org/10.1080/02697 45090 28273 29
Chatman, D.G., Broaddus, A., Spevack, A.: Are movers irrational? On travel patterns, housing character-
istics, social interactions, and happiness before and after a move. Travel Behav. Soc. 16, 262–271
(2019). https ://doi.org/10.1016/j.tbs.2018.11.004
Clark, B., Chatterjee, K., Martin, A., Davis, A.: How commuting affects subjective wellbeing. Transporta-
tion 2357(4), 100 (2019). https ://doi.org/10.1007/s1111 6-019-09983 -9
Dauth, W., Haller, P.: Berufsbedingte Pendeln zwischen Wohn-und Arbeitsort: Klarer Trend zu längeren
Pendeldistanzen. In: IAB-Kurzbericht, (10/2018) (2018)
De Vos, J., Schwanen, T., Van Acker, V., Witlox, F.: Travel and subjective well-being: a focus on find-
ings, methods and future research needs. Transp. Rev. 33(4), 421–442 (2013). https ://doi.
org/10.1080/01441 647.2013.81566 5
De Vos, J., Ettema, D., Witlox, F.: Effects of changing travel patterns on travel satisfaction: a focus
on recently relocated residents. Travel Behav. Soc. 16, 42–49 (2019). https ://doi.org/10.1016/j.
tbs.2019.04.001
DeLoach, S.B., Tiemann, T.K.: Not driving alone? American commuting in the twenty-first century. Trans-
portation 39(3), 521–537 (2012). https ://doi.org/10.1007/s1111 6-011-9374-5
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

2155
Transportation (2021) 48:2127–2158
1 3
Einig, K., Pütz, T.: Regionale Dynamik der Pendlergesellschaft: Entwicklung von Verflechtungsmustern
und Pendeldistanzen. Informationen zur Raumentwicklung 3(2), 73–91 (2007)
Epifani, P., Gancia, G.A.: Trade, migration and regional unemployment. Reg. Sci. Urban Econ. 35(6), 625–
644 (2005). https ://doi.org/10.1016/j.regsc iurbe co.2004.09.003
Ettema, D., Friman, M., Gärling, T., Olsson, L.E., Fujii, S.: How in-vehicle activities affect work commut-
ers’ satisfaction with public transport. J. Transp. Geogr. 24, 215–222 (2012). https ://doi.org/10.1016/j.
jtran geo.2012.02.007
Eurostat.: Employment and commuting by NUTS 2 regions (1000). https ://appss o.euros tat.ec.europ a.eu/nui/
show.do?datas et=lfst_r_lfe2e comm&lang=en (2019). Accessed 21 June 2020
Evans, G.W., Wener, R.E.: Rail commuting duration and passenger stress. Health Psychol. 25(3), 408–412
(2006). https ://doi.org/10.1037/0278-6133.25.3.408
Ewing, R., Pendall, R., Chen, D.: Measuring sprawl and its transportation impacts. Trans. Res. Rec. 1831(1),
175–183 (2003). https ://doi.org/10.3141/1831-20
Fahrmeir, L., Kneib, T., Lang, S., Marx, B.: Regression: Models, Methods and Applications. Springer,
Dordrecht (2013). https ://doi.org/10.1007/978-3-642-34333 -9
Ferguson, E.: The rise and fall of the American carpool: 1970–1990. Transportation 24(4), 349–376 (1997).
https ://doi.org/10.1023/A:10049 28012 320
Forschungsdatenzentren der Statistischen Ämter des Bundes und der Länder (a): Metadatenreport. Teil I:
Allgemeine und methodische Informationen zum Mikrozensus (EVAS-Nummer: 12211, 12212,
12213). Düsseldorf 2019 (2019)
Forschungsdatenzentren der Statistischen Ämter des Bundes und der Länder (b): Mikrozensus 2012, On-
Site, Version 1 (2012). https ://doi.org/10.21242 /12211 .2012.00.00.1.1.1
Fuchs, M., Ludewig, O., Weyh, A.: Arbeitslosigkeit in Ost und West: Unterschiede verschwimmen immer
mehr. In: IAB-Forum, 2015(1) (2015)
Gardner, B., Abraham, C.: What drives car use? A grounded theory analysis of commuters’ reasons for
driving. Transp. Res. Part F: Traffic Psychol. Behav. 10(3), 187–200 (2007). https ://doi.org/10.1016/j.
trf.2006.09.004
Gottholmseder, G., Nowotny, K., Pruckner, G.J., Theurl, E.: Stress perception and commuting. Health Econ.
18(5), 559–576 (2009). https ://doi.org/10.1002/hec.1389
Granato, N., Haas, A., Hamann, S., Niebuhr, A.: Arbeitskraftemobilität in Deutschland - Qualifikations-
spezifische Befunde regionaler Wanderungs-und Pendlerströme. Raumforschung und Raumordnung
67(1), 21–33 (2009). https ://doi.org/10.1007/BF031 83140
Green, A.E.: A question of compromise? Case study evidence on the location and mobility strategies of dual
career households. Reg. Stud. 31(7), 641–657 (1997). https ://doi.org/10.1080/00343 40975 01307 31
Groot, S., Groot, H., Veneri, P.: The educational bias in commuting patterns: micro-evidence for the nether-
lands. SSRN Electron. J. (2012). https ://doi.org/10.2139/ssrn.21199 29
Gutierrez, F.: Commuting patterns, the spatial distribution of jobs and the gender pay gap in the U.S. SSRN
Electron. J. (2018). https ://doi.org/10.2139/ssrn.32906 50
Haas, A.: Mobilität zwischen Regionen: Pendlerströme Fließen überwiegend von Ost nach West. In: IAB-
Forum (2012/2), pp 68–73 (2012)
Haas, A., Hamann, S.: Pendeln -ein zunehmender Trend, vor allem bei Hochqualifizierten. In: IAB-Kurz-
bericht, (2008/6) (2008)
Habib, K.N.: Household-level commuting mode choices, car allocation and car ownership level choices of
two-worker households: the case of the city of Toronto. Transportation 41(3), 651–672 (2014)
He, S.Y., Hu, L.: Telecommuting, income, and out-of-home activities. Travel Behav. Soc. 2(3), 131–147
(2015). https ://doi.org/10.1016/j.tbs.2014.12.003
Hjorthol, R., Vågane, L.: Allocation of tasks, arrangement of working hours and commuting in differ-
ent Norwegian households. J. Transp. Geogr. 35, 75–83 (2014). https ://doi.org/10.1016/j.jtran
geo.2014.01.007
Horner, M.W., Mefford, J.N.: Investigating urban spatial mismatch using job-housing indicators to model
home-work separation. Environ. Plan. A: Econ. Space 39(6), 1420–1440 (2007). https ://doi.
org/10.1068/a3744 3
Horner, M.W., Schleith, D.K., Widener, M.J.: An analysis of the commuting and jobs-housing pat-
terns of older adult workers. Prof. Geogr. 67(4), 575–585 (2015). https ://doi.org/10.1080/00330
124.2015.10540 18
International Labour Office (ILO): International Standard Classification of Occupations 2008 (ISCO-08):
Structure, group definitions and correspondence tables (2012)
Kakihara, M., Sørensen, C.: Practising mobile professional work: tales of locational, operational, and inter-
actional mobility. Info 6(3), 180–187 (2004). https ://doi.org/10.1108/14636 69041 05495 07
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

2156
Transportation (2021) 48:2127–2158
1 3
Kanaroglou, P.S., Higgins, C.D., Chowdhury, T.A.: Excess commuting: a critical review and comparative
analysis of concepts, indices, and policy implications. J Transp Geogr 44, 13–23 (2015). https ://doi.
org/10.1016/j.jtran geo.2015.02.009
Kim, T.-K., Horner, M.W., Marans, R.W.: Life cycle and environmental factors in selecting residential and
job locations. Hous. Stud. 20(3), 457–473 (2005). https ://doi.org/10.1080/02673 03050 00623 35
Kley, S.: Gefährdet Pendelmobilität die Stabilität von Paarbeziehungen? Einflüsse von Erwerbskonstella-
tionen und Haushaltsarrangements in Ost-und Westdeutschland auf die Trennungswahrscheinlichkeit
von Paaren. Zeitschrift für Soziologie 41, 356–374 (2012)
Kohl, H.: Zum Wandel berufsbedingter Mobilität in der Wissensökonomie. Geographische Rundschau
66(12), 26–31 (2014)
Korsu, E., Le Néchet, F.: Would fewer people drive to work in a city without excess commuting? Explora-
tions in the Paris metropolitan area. Transp. Res. Part A: Policy Pract. 95, 259–274 (2017). https ://
doi.org/10.1016/j.tra.2016.10.030
Krause, P.: 30 Jahre seit dem Mauerfall: Fortschritte und Defizite bei der Angleichung der Lebensverhält-
nisse in Ost-und Westdeutschland. DIW-Wochenbericht 86(45), 827 (2019)
Kümmerling, A.: Beschäftigungsentwicklung und Arbeitszeiten von Frauen in Deutschland – eine Erfolgss-
tory? Zeitschrift für Arbeitswissenschaft 69(1), 23–29 (2015). https ://doi.org/10.1007/BF033 73933
Layman, C.C., Horner, M.W.: Comparing methods for measuring excess commuting and jobs-hous-
ing balance. Transp. Res. Rec. J. Transp. Res. Board 2174(1), 110–117 (2010). https ://doi.
org/10.3141/2174-15
Manderscheid, K.: Mobile Ungleichheiten. Eine sozial-und infrastrukturelle Differenzierung des Mobilität-
stheorems. Österreichische Zeitschrift für Soziologie 41(1), 71–96 (2016). https ://doi.org/10.1007/
s1161 4-016-0192-z
McQuaid, R.W., Chen, T.: Commuting times: the role of gender, children and part-time work. Res. Transp.
Econ. 34(1), 66–73 (2012)
Morris, E.A., Zhou, Y.: Are long commutes short on benefits? Commute duration and various manifesta-
tions of well-being. Travel Behav. Soc. 11, 101–110 (2018). https ://doi.org/10.1016/j.tbs.2018.02.001
Motte-Baumvol, B., Bonin, O., Belton-Chevallier, L.: Who escort children: mum or dad? Exploring gender dif-
ferences in escorting mobility among parisian dual-earner couples. Transportation 44(1), 139–157 (2017)
Nobis, C., Kuhnimhof, T.: Mobilität in Deutschland – MiD. Ergebnisbericht. BMVI, infas, DLR, IVT. Ed. by
2018 (2018)
Nobis, C., Kuhnimhof, T., Follmer, R., Bäumer, M.: Mobilität in Deutschland -MiD. Zeitreihenbericht 2002
-2008 -2017. Ergebnisbericht. BMVI, infas, DLR, IVT, infas 360 (2019)
OECD: Classifying educational programmes—manual for isced-97 implementation in OECD countries (1999)
OECD: Working age population (2020). https ://data.oecd.org/pop/worki ng-agepo pulat ion.htm. Accessed 28
Mar 2020
Oakil, A.T.M., Nijland, L., Dijst, M.: Rush hour commuting in the Netherlands: gender-specific household
activities and personal attitudes towards responsibility sharing. Travel Behav. Soc. 4, 79–87 (2016). https
://doi.org/10.1016/j.tbs.2015.10.003
Pfaff, S.: Pendeln oder umziehen? Mobilitätsentscheidungen in Deutschland zwischen 2000 und 2009.
Zeitschrift für Soziologie 41(6), 458–477 (2012)
Pfaff, S.: Pendelentfernung, Lebenszufriedenheit und Entlohnung: Eine Längsschnittuntersuchung mit den
Daten des SOEP von 1998 bis 2009. Zeitschrift für Soziologie 43(2), 113–130 (2014)
Potter, S., Enoch, M., Rye, T., Black, C., Ubbels, B.: Tax treatment of employer commuting support: an interna-
tional review. Transp. Rev. 26(2), 221–237 (2006)
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Comput-
ing, Vienna (2019)
Rapino, M.A., Cooke, T.J.: Commuting, gender roles, and entrapment: a national study utilizing spatial
fixed effects and control groups. Prof. Geogr. 63(2), 277–294 (2011). https ://doi.org/10.1080/00330
124.2010.54779 0
Reichelt, M., Haas, A.: Commuting farther and earning more? How employment density moderates workers’
commuting distance. In: IAB-Discussion Paper, (2015/33), pp. 1–30 (2015)
Renkow, M., Hoover, D.: Commuting, migration, and rural–urban population dynamics. J. Reg. Sci. 40(2), 261
(2000)
Roberts, J., Hodgson, R., Dolan, P.: It’s driving her mad: gender differences in the effects of commuting on psy-
chological health. J. Health Econ. 30(5), 1064–1076 (2011)
Rüger, H., Schulze, A.: Zusammenhang von beruflicher Pendelmobilität mit Stresserleben und Gesundheit.
Prävention und Gesundheitsförderung 11(1), 27–33 (2016). https ://doi.org/10.1007/s1155 3-015-0521-2
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

2157
Transportation (2021) 48:2127–2158
1 3
Rüger, H., Sulak, H.: Wochenendpendeln von Erwerbstätigen in Deutschland: Analysen mit den Mikrozensen
1991 bis 2012. Raumforschung und Raumordnung 75(5), 413–427 (2017). https ://doi.org/10.1007/s1314
7-01704 96-x
Ruppenthal, S., Lück, D.: Jeder fünfte Erwerbstätige ist aus beruflichen Gründen mobil: berufsbedingte räumli-
che Mobilität im Vergleich. Informationsdienst Soziale Indikatoren 42, 1–5 (1999)
Sandow, E.: Til work do us part: the social fallacy of long-distance commuting. Urban Stud. 51(3), 526–543
(2014). https ://doi.org/10.1177/00420 98013 49828 0
Sayer, L.C., Gauthier, A.H., Furstenberg, F.F.: Educational differences in parents’ time with children: cross-
national variations. J. Marriage Fam. 66(5), 1152–1169 (2004). https ://doi.org/10.1111/j.00222
445.2004.00084 .x
Scheiner, J.: Randwanderung, Pendeln und Geschlecht in einer polyzentralen Region. Raumforschung und Rau-
mordnung 74(2), 117–134 (2016). https ://doi.org/10.1007/s1314 7-016-0388-5
Scheiner, J., Holz-Rau, C.: Women’s complex daily lives: a gendered look at trip chaining and activity pattern
entropy in Germany. Transportation 44(1), 117–138 (2017). https ://doi.org/10.1007/s1111 6-015-9627-9
Schleith, D., Widener, M.J., Kim, C., Horner, M.W.: Categorizing urban form for the largest metro regions in
the U.S. using the excessive commuting framework. Built Environ. 45(4), 450–461 (2019). https ://doi.
org/10.2148/benv.45.4.450
Schleith, D., Horner, M.W.: Commuting, job clusters, and travel burdens. Transp. Res. Rec. J. Transp. Res.
Board 2452(1), 19–27 (2014). https ://doi.org/10.3141/2452-03
Schleith, D., Widener, M., Kim, C.: An examination of the jobs-housing balance of different categories of work-
ers across 26 metropolitan regions. J. Transp. Geogr. 57, 145–160 (2016). https ://doi.org/10.1016/j.jtran
geo.2016.10.008
Schneider, N.F., Limmer, R., Ruckdeschel, K.: Mobil, flexibel, gebunden: Familie und Beruf in der mobilen
Gesellschaft. Campus-Verl, Frankfurt/Main (2002)
Shearmur, R., Motte, B.: Weak ties that bind. Urban Aff. Rev. 44(4), 490–524 (2009). https ://doi.
org/10.1177/10780 87408 32259 2
Strambach, S., Kohl, H.: Mobilitätsdynamiken und Wissensarbeit - zum Wandel berufsbedingter zirkulärer
Mobilität. Raumforschung und Raumordnung 73(4), 257–268 (2015). https ://doi.org/10.1007/s1314
7-015-0355-6
Stutzer, A., Frey, B.: Commuting and life satisfaction in Germany. Informationen zur Raumentwicklung 3(2),
1–11 (2007)
Stutzer, A., Frey, B.: Stress that doesn’t pay: the commuting paradox. Scand. J. Econ. 110(2), 339–366 (2008).
https ://doi.org/10.1111/j.1467-9442.2008.00542 .x
Suedekum, J.: Increasing returns and spatial unemployment disparities. Pap. Reg. Sci. 84(2), 159–181 (2005).
https ://doi.org/10.1111/j.1435-5957.2005.00011 .x
Sultana, S.: Job/housing imbalance and commuting time in the Atlanta Metropolitan Area: exploration of causes
of longer commuting time. Urban Geogr. 23(8), 728–749 (2002)
Sultana, S.: Commuting constraints of black female workers in atlanta: an examination of the spatial mismatch
hypothesis in married-couple, dual-earner households. Southeast. Geogr. 43(2), 249–259 (2003)
Sultana, S.: Effects of married-couple dual-earner households on metropolitan commuting: evi-
dence from the Atlanta Metropolitan Area. Urban Geogr. 26(4), 328–352 (2005). https ://doi.
org/10.2747/0272-3638.26.4.328
Surprenant-Legault, J., Patterson, Z., El-Geneidy, A.M.: Commuting trade-offs and distance reduction in
two-worker households. Transp. Res. Part A: Policy Pract. 51, 12–28 (2013). https ://doi.org/10.1016/j.
tra.2013.03.003
Suzuki, T., Lee, S.: Jobs-housing imbalance, spatial correlation, and excess commuting. Transp. Res. Part A:
Policy Pract. 46(2), 322–336 (2012). https ://doi.org/10.1016/j.tra.2011.10.004
Swärdh, J.-E., Algers, S.: Willingness to accept commuting time within the household: stated preference evi-
dence. Transportation 43(2), 219–241 (2016). https ://doi.org/10.1007/s1111 6-014-9573-y
Van Ommeren, J., Rietveld, P., Nijkampb, P.: Job moving, residential moving, and commuting: a search per-
spective. J. Urban Econ. 46(2), 230–253 (1999)
Vanoutrive, T., van de Vijver, E., van Malderen, L., Jourquin, B., Thomas, I., Verhetsel, A., Witlox, F.: What
determines carpooling to workplaces in Belgium: location, organisation, or promotion? J. Transp. Geogr.
22, 77–86 (2012). https ://doi.org/10.1016/j.jtran geo.2011.11.006
Venables, W.N., Ripley, B.D.: Modern Applied Statistics with S, 4th edn. Springer, New York (2002)
Vuk, G., Bowman, J.L., Daly, A., Hess, S.: Impact of family in-home quality time on person travel demand.
Transportation 43(4), 705–724 (2016). https ://doi.org/10.1007/s1111 6-015-9613-2
Wagner, M., Mulder, C.H.: Spatial mobility, family dynamics, and housing transitions. KZfSS Kölner
Zeitschrift für Soziologie und Sozialpsychologie 67(S1), 111–135 (2015). https ://doi.org/10.1007/s1157
7-015-0327-4
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

2158
Transportation (2021) 48:2127–2158
1 3
Weber, J., Sultana, S.: Employment sprawl, race and the journey to work in Birmingham, Alabama. Southeast.
Geogr. 48(1), 53–74 (2008). https ://doi.org/10.1353/sgo.0.0014
Wind, S.: ACTUM WP2: making daily mobility: phase 1 analysis report. English, vol. 74 (2012)
Winkelmann, U.: Manche pendeln weit: Berufspendler im Bundesländervergleich. Statistisches Monatsheft 4,
40–43 (2010)
Wood, B.S., Horner, M.W., Duncan, M., Valdez-Torres, Y.: Aging populations and transit-oriented develop-
ment: socioeconomic, demographic, and neighborhood trends from 2000 and 2010. Transp. Res. Rec. J.
Transp. Res. Board 2598(1), 75–83 (2016). https ://doi.org/10.3141/2598-09
Wrede, M.: Heterogeneous skills, migration, and commuting. Pap. Reg. Sci. 92(2), 345–360 (2013). https ://doi.
org/10.1111/j.1435-5957.2012.00471 .x
Yang, J.: Policy implications of excess commuting: examining the impacts of changes in US metropolitan spa-
tial structure. Urban Stud. 45(2), 391–405 (2008). https ://doi.org/10.1177/00420 98007 08596 9
Ye, R., de Vos, J., Ma, L.: Analysing the association of dissonance between actual and ideal commute time and
commute satisfaction. Transp. Res. Part A: Policy Pract. 132, 47–60 (2020). https ://doi.org/10.1016/j.
tra.2019.10.011
Zarabi, Z., Manaugh, K., Lord, S.: The impacts of residential relocation on commute habits: a qualitative per-
spective on households’ mobility behaviors and strategies. Travel Behav. Soc. 16, 131–142 (2019). https
://doi.org/10.1016/j.tbs.2019.05.003
Zhou, J., Murphy, E.: Day-to-day variation in excess commuting: an exploratory study of Brisbane, Australia. J.
Transp. Geogr. 74, 223–232 (2019). https ://doi.org/10.1016/j.jtran geo.2018.11.014
Zhu, Z., Li, Z., Chen, H., Liu, Y., Zeng, J.: Subjective well-being in China: how much does commuting matter?
Transportation 46(4), 1505–1524 (2019). https ://doi.org/10.1007/s1111 6-017-9848-1
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institutional affiliations.
Moritz Kersting Studied Economics at the University of Göttingen and Halle. Trainee in the office of the
member of parliament Mark Helfrich. Visiting researcher at the Max Planck Institute for Dynamics and
Self-Organization (MPI DS ) at the Department of Complex Fluids (DCF) in the Next Generation Mobility
Group (NGM).
Eike Matthies Studied Geography at the University of Göttingen and Regional Management and Business
Development at the Applied University of Science and Arts in Göttingen (HAWK). Visiting researcher at
the Max Planck Institute for Dynamics and Self-Organization (MPI DS) at the Department of Complex Flu-
ids (DCF) in the Next Generation Mobility Group (NGM). PhD Student in the Göttingen Graduate School
of Social Sciences (GGG) at the Chair of Economic Policy and Small- and medium-sized enterprises.
Prof. Dr. Jörg Lahner Studied Economics at the University of Göttingen, Hanover and Madrid. PhD
at the Institute for Small- and medium-sized enterprises (IFH) at the University of Göttingen. Professor
at the Applied University of Science and Arts Göttingen (HAWK) in Business Promotion and Regional
Economics.
Dr. Jan Schlüter Studied Economics and Physics at the University of Göttingen, the Eötvös University in
Budapest and the Copenhagen Business School. Diploma thesis at the Department of Materials Physics
at the Eötvös University in Budapest. Max-Planck-Society Scholarship at the Max Planck Research Group
(MPRG) of Complex Dynamics and Turbulence. Researcher at the Nonlinear Dynamics and Turbulence
Group at the Institute of of Science and Technology Austria (IST Austria). Next Generation Mobility Group
(NGM) Leader at the Department of Complex Fluids (DCF) in the Max Planck Institute for Dynamics and
Self-Organization (MPI DS).
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